Genome-wide association methods based on linkage disequilibrium (LD) offer a promising approach to detect genetic variations that are responsible for complex human diseases, such as hypertension, diabetes, obesity, cancers, etc. Approaches based on haplotypes may provide additional power to map disease genes than those based on single markers. More importantly, haplotypes may lead to insights on the factors influencing the dependencies among genetic markers, i.e. linkage disequilibrium (LD), and such insights may provide information essential to understand human evolution and may capture cis-interactions between 2 or more causal variants. However, the haplotype analysis using a large number of tightly linked SNPs is just being developed and poses great challenges to scientists. Furthermore, most existing methods have not considered the haplotype structure that will soon be provided by the HapMap project and have not been evaluated in this context. The overall goal of this project is to develop statistical and computational tools and methods for the analysis of haplotypes in linkage disequilibrium mapping of complex disease genes. The specific objectives of this project are: (1) Develop efficient algorithms to estimate haplotype frequencies and determine haplotype configurations in general pedigrees for a large number of tightly linked genetic markers with recombinants. (2) Define new test statistics based on haplotype sharing for mapping genes responsible for complex human diseases. (3) Assess the power using tag SNPs in linkage disequilibrium mapping of genes that are responsible for qualitative and quantitative traits. In this context, different methods for tag SNP selection will be compared and the effect of several critical issues in designing efficient and effective algorithms for tag SNP selection will be investigated. (4) Release user-friendly software to the scientific community. The proposed methods are expected to aid the discovery of genes that are responsible for complex human diseases and finally enhance our ability to understand them. [unreadable] [unreadable] [unreadable]